Liang Bao-juan
Papers
2
Total Citations
17
H-Index
2
About
Liang Bao-juan is a researcher whose work sits at the intersection of robotics, sensor fusion, and smart home technology. Her primary research areas include visual simultaneous localization and mapping (SLAM), multi-sensor integration, and human-robot interaction for social robotics. She has made significant contributions to advancing how robots perceive and navigate unstructured environments. Her most cited work, "Comparison and Analysis of Feature Method and Direct Method in Visual SLAM Technology for Social Robots" (2018, 10 citations), provides a critical evaluation of visual odometry techniques, offering practical insights for deploying social robots in complex, real-world settings. In a second influential paper (2018, 7 citations), she proposed a home assistant-based collaborative framework that tackles the critical challenge of interoperability among diverse smart home devices. By enabling multi-sensor fusion across incompatible communication standards, her work directly addresses a major barrier to the widespread adoption of smart home ecosystems. Liang’s research is notable for its practical, application-driven focus, bridging theoretical advances in SLAM and sensor fusion with tangible solutions for assistive and social robotics. Her contributions are particularly valuable for researchers and engineers working to create more autonomous, context-aware robots that can seamlessly operate within human-centered environments.
Research Focus
Key Achievements
Top Papers
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- 2